English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We present an approach for full 3D scene reconstruction from a single unseenimage. We train on dataset of realistic non-watertight scans of scenes. Ourapproach predicts a distance function, since these have shown promise inhandling complex topologies and large spaces. We identify and analyze two keychallenges for predicting such image conditioned distance functions that haveprevented their success on real 3D scene data. First, we show that predicting aconventional scene distance from an image requires reasoning over a largereceptive field. Second, we analytically show that the optimal output of thenetwork trained to predict these distance functions does not obey all thedistance function properties. We propose an alternate distance function, theDirected Ray Distance Function (DRDF), that tackles both challenges. We showthat a deep network trained to predict DRDFs outperforms all other methodsquantitatively and qualitatively on 3D reconstruction from single image onMatterport3D, 3DFront, and ScanNet.

What's Behind the Couch? Directed Ray Distance Functions (DRDF) for 3D Scene Reconstruction